Advances in Meteorology

Volume 2016 (2016), Article ID 3768242, 14 pages

http://dx.doi.org/10.1155/2016/3768242

## A Hybrid Model Based on Ensemble Empirical Mode Decomposition and Fruit Fly Optimization Algorithm for Wind Speed Forecasting

^{1}Key Laboratory of Arid Climatic Change and Reducing Disaster of Gansu Province, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China^{2}School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China

Received 26 February 2016; Revised 10 July 2016; Accepted 4 August 2016

Academic Editor: Ferhat Bingol

Copyright © 2016 Zongxi Qu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

As a type of clean and renewable energy, the superiority of wind power has increasingly captured the world’s attention. Reliable and precise wind speed prediction is vital for wind power generation systems. Thus, a more effective and precise prediction model is essentially needed in the field of wind speed forecasting. Most previous forecasting models could adapt to various wind speed series data; however, these models ignored the importance of the data preprocessing and model parameter optimization. In view of its importance, a novel hybrid ensemble learning paradigm is proposed. In this model, the original wind speed data is firstly divided into a finite set of signal components by ensemble empirical mode decomposition, and then each signal is predicted by several artificial intelligence models with optimized parameters by using the fruit fly optimization algorithm and the final prediction values were obtained by reconstructing the refined series. To estimate the forecasting ability of the proposed model, 15 min wind speed data for wind farms in the coastal areas of China was performed to forecast as a case study. The empirical results show that the proposed hybrid model is superior to some existing traditional forecasting models regarding forecast performance.

#### 1. Introduction

The world’s current sources of fossil fuels will eventually be depleted, mainly due to high demand and, in some situations, extravagant consumption [1]. The recently posted Energy Outlook 2035 of British Petroleum predicts that primary energy consumption will increase by 37% between 2013 and 2035, with growth averaging 1.4% per year. Approximately 96% of the expected growth will be in countries that are not members of the Organization for Economic Cooperation and Development (OECD), with energy consumption growing at 2.2% per year [2]. According to some statistics, energy demand worldwide will grow rapidly by one-third from 2010 to 2035, and China and India will become the largest contributors, accounting for 50 percent of the growth during that period. Moreover, China is expected to be the largest oil importer by 2020 [2, 3]. To cope with the growing demand for energy, countries such as China can look to renewable energy sources to provide an opportunity for sustainable development. The significance of renewable sources was recently underpinned by a plethora of advocates and reports, which have mostly focused on wind energy studied by the related institutions and energy commissions of several countries [2, 4–7]. According to reports from the China National Renewable Energy Center (CNREC), wind resources in China are rich and promising prospects, carrying a potential of more than 3.0 TW, mostly in the Three North Areas, with an onshore potential of more than 2.6 TW. Before 2020, land-based wind power will dominate, with offshore wind power in the demonstration status. Furthermore, the annual discharge of carbon dioxide will be reduced to 1.5 billion tons and 3.0 billion tons in 2050 in the conservative and aggressive scenarios, and an estimated 720 000 jobs and 1 440 000 jobs will be created, respectively [4, 5]. Based on these figures, wind energy should be regarded as an appealing energy option because it is both abundant and environmentally friendly; as such, wind energy will be able to satisfy the growing demand for electricity.

Wind energy has great influence on power grid security, power system operation, and market economics due to its intermittent nature, especially in areas with high wind power penetration. Thus, the analysis and assessment of wind energy are a meaningful but markedly difficult task for research. Because wind power generation hinges on wind speed, obtaining accurate wind speeds is important. To improve the precision of wind speed predictions, numerous methods have been proposed and developed in recent decades. These methods can be divided into three general types: physical models, conventional statistical models, and artificial intelligence models [8–11]. Physical models use weather prediction data, such as temperature, pressure, orography, obstacles, and surface roughness, for the best forecasting accuracy but are poor at short-term wind speed simulation. Conventional statistical models, in contrast, draw on vast historical data based on mathematical models usually involving conventional time series analysis, such as ARMA, ARIMA, or seasonal ARIMA models [12, 13], and achieve more accurate short-term wind speed predictions than physical models. However, conventional statistical models are imperfect. The fluctuating and intermittent characteristics of wind speed sequences require more complicated functions to capture the nonlinear relationships rather than assuming a linear correlation structure [14]. Given the development of statistical models along with the advent of artificial intelligence techniques, artificial intelligence models, including artificial neural networks (ANNs) and other mixed methods, have been proposed and are used in the field of wind speed forecasting [15–20]. For instance, because of the chaotic nature of wind time series, Alanis et al. [15] proposed a higher order neural network (HONN) based on an extended Kalman filter for model training, which provides accurate one-step-ahead predictions. Guo et al. [20] proposed a hybrid wind speed forecasting method employing a backpropagation (BP) neural network and seasonal exponential adjustment to remove seasonal effects from actual wind speed datasets. Wang et al. [21] exploited a radial basis function (RBF) neural network for wind speed prediction, and the effectiveness of this method was proved by a practical case. Zhou et al. [17] proposed a prediction method based on a support vector machine (SVM), for short-term wind speed prediction. De Giorgi et al. [19] adopted the ANNs to forecast wind speeds and compared them to the linear time-series-based model, with the ANNs providing a robust approach for wind prediction. All of these methods have improved the precision of wind speed predictions to some extent.

However, wind speed time series are highly noisy and unstable; therefore, using the primary wind speed series directly to establish prediction models is subject to large errors [22–24]. To build an effective prediction model, the features of original wind speed datasets must be fully analyzed and considered. The ensemble empirical mode decomposition (EEMD) [25] is an advanced, effective technology, which makes up for the deficiency of EMD [26] and has certain advantages over other typical decomposition approaches such as the wavelet decomposition and the Fourier decomposition [27]. With direct, intuitive, empirical, and adaptive data processing, EEMD was especially devised for nonlinear and complicated signal sequences, such as wind speed series. For example, Hu et al. [22] proposed a hybrid method based on the EEMD to disassemble the original wind speed datasets into a series of independent Intrinsic Mode Functions (IMFs) and use SVM to predict the values for IMFs in different frequencies. Jiang et al. [28] also proposed a hybrid model for high-speed rail demand forecasting based on EEMD, in which the original series are decomposed into certain signals with different frequencies and then the grey support vector machine (GSVM) is employed for forecasting. Zhou et al. [29] additionally proposed a hybrid method based on EEMD and the generalized regression neural network (GRNN). In this method, the original data are decomposed into different IMFs with corresponding frequencies and the residue component by EEMD, and then each component is taken as an input to establish GRNN forecasting model.

Each of the aforementioned models only employs a single ANN model to predict all of the signal sequences decomposed by EEMD; nevertheless, different signals have different characteristics, meaning that a simple individual model can no longer adapt to all properties of the data. Moreover, previous literature has not addressed which features are best suited for choosing the most appropriate approach. Thus, in our study, we propose a hybrid model based on a model selector that combines RBF, GRNN, and SVR to address signal data series with different characteristics to further improve forecasting accuracy.

In existing neural network training structures, model parameters are very vital factors affecting prediction precision, and different types of data require different parameters. The genetic algorithm (GA) and particle swarm optimization (PSO) algorithms are the most common approaches to optimize the parameters of neural network structures. Liu et al. [30] used the genetic algorithm to determine the weight coefficients of a combined model for wind speed forecasting. Zhao et al. [31] developed a combined model for energy consumption prediction based on model parameters optimization with the genetic algorithm. Ren et al. [32] applied the particle swarm optimization to set weight coefficients of a forecasting model for 6-hour wind speed forecasting. However, these meta-heuristic algorithms have the drawbacks of being hard to understand and achieving the global optimal solution slowly. The fruit fly optimization algorithm (FOA) [33] was a new optimization and evolutionary computation technique, which has distinct advantages in its simple computational process, fewer parameters to be fine-tuned, and stronger ability to search for global optimal solutions and outperforms other metaheuristic algorithms [34, 35]. In our study, we introduce the FOA algorithm to automatically determine the necessary parameters of the RBF, GRNN, and SVR models to achieve better performance.

The rest of the paper is organized as follows. Section 2 briefly introduces related methods while Section 3 describes the proposed hybrid approach in detail. Section 4 describes the dataset used for this study and discusses the forecasting results of proposed model compared with other prediction models. Section 5 concludes the work.

#### 2. Related Methodology

This section briefly introduces EEMD, FOA, and three classical forecasting models: RBF, GRNN, and SVR, which will be used in our research.

##### 2.1. RBF

The radial basis function (RBF) neural network is a type of feedforward network developed by Broomhead and Lowe [36]. This type of neural network is based on a supervised algorithm and has been widely applied to interpolation regression, prediction, and classification [37–39]. It has three layers of architecture, where there are no weights between the input hidden layers, and each hidden unit implements a radial-activated function. The Gaussian activation function is used in each neuron at the hidden layer, which can be formulated aswhere is the th input sample, is the mean value of the th hidden unit presenting the center vector, is the covariance of the th hidden unit denoting the width of the RBF kernel function, and is the number of training samples.

The network output layer is linear so that the th output is an affine function that can be expressed aswhere is the weight between the th output and th hidden unit, is the biased weight of the th output, and is the number of hidden nodes.

##### 2.2. GRNN

The general regression neural network (GRNN), first proposed by Specht [40], is a very powerful computational technique used to solve nonlinear approximation problems based on nonlinear regression theory. The advantages of GRNNs include its good feasibility, simple structure, and fast convergence rate. It consists of four layers, and its basic principles are presented in Figure 1.